 So what I'm going to walk you through is just some initial thoughts on the information-driven enterprise, where we are today from an information perspective, what's important, and why big data and information in general matters and to whom. I will call out some differences between the types of systems that we are dealing with. We refer to it more as systems of record versus systems of engagement. Both of them driving techniques that are used to predict, to apply and leverage big data from an applied complex and analytics to realize information value. And then go through some use cases as well as some scenarios specific to various industries just to make a point about the pervasive impact that big data has had. So this is based on a forester study. The different use cases that actually require big data and highlighted certain use cases that apply more, like sentiment analysis, mobile offers, micro segmentation, next best action for customers and so on. The interesting point is that many of these are seeing in production and there is investigation going on and investments, but suffice it to say that big data is having an impact and enterprises are reacting. It is just that the extent to which enterprises are reacting varies, but everyone is doing something or wanting to do something, kicking the tires, so it is definitely out there. A synthesis of the various findings from that study, so here are some key points. Basically businesses are relying on data. The question is, are they really doing something about it because you will see a quote later, big data really doesn't matter without analytics being applied to it, so yes they want to use the data and how effective are they is what is a good question to ask themselves. There are the depositories of big data and the connections between the right data and them being made in a timely fashion can also inhibit the benefits that enterprises can realize. Therefore it is not just about using the data but also making the right connections and applying the right types of analytics. So as the volume of data grows, continues to grow exponentially, the techniques that are at play and that are evolving for analyzing the data will also significantly influence the cost. The more informational value that can be realized from the data, the easier it is to justify the cost of the investment in analyzing the data, minus the analysis you are not going to get the business value out of the information. So first, the big data and security, different dimensions to that. So how much can you rely? I mean the first one says that businesses are letting data guide their strategic directions to the extent that the data integrity is maintained and you can really rely on the data collected. So accuracy of the data is definitely an issue and trust is built up based upon how accurate the data is. Finally, big data has also opened up the need for added new roles and also opened up the need for existing roles to acquire new skills. So in other words, enterprises have to revitalize the resources, the employees, the skill sets of the employees in order to adjust and align with the emerging technologies and techniques, paradigms that big data has brought forth. So based upon that backdrop, here is the overtake, so to speak, from an HP perspective on the new style of IT that has emerged. So on the left, what you see are the legacy applications that are more the systems of record. These are the well-tested, decades-old systems that have stood the test of time and deliver on the functionality and have been basically the functional abilities of these applications have been consumed more and more. Let us just say, you will see it in the next slide, but 80% we are there in using what these applications can deliver. It does take resources, does consume a good chunk of the IT budget even from a maintenance perspective. They are prime candidates with the right business perspective on migration to the cloud, modernization for sound business reasons. And on the right, you have the systems of engagement where social media and mobility have actually resulted in multiple connection points. It's a connected world out there where you can immediately reach out to the people of your choice and broadcast and share information, share data in the fraction of a second. And it is not so much about just you sharing the information, but you have access to the data that is shared by others too. That being said, the sharing is just happening. It is beyond control. There is really no limit to the growth of data and new terms are being invited way beyond petabytes and so on. We are talking brontobytes and I'm sure the plans are already underway to kind of go to the next unit of measure because it is just growing beyond control. So scale is a challenge and security is also a prime consideration because the more data that you make available, adversaries and predators are out there just waiting to pounce upon that data and then kind of synthesize that. Ironically, they could also apply analytical techniques to realize information that they can realize value from as predators. So these are all some of the challenges but then systems of engagement happen even without IT taking a proactive role. It is more driven by the consumers. So that's really what you are dealing with, the systems of record backdrop with the systems of engagement emerging and the two are kind of being brought together through various paradigms like cloud, mobility, security, information optimization and so on. So depending upon the enterprise and your mindset in looking at what's out there and how it is impacting you, it could be viewed as there are risks but at the same time, it could also be viewed as opportunities. So the question remains, what are enterprises doing about it? A comparative analysis between systems of record and systems of engagement. So the systems of record have a strong foundation. They have pretty much delivered on, like I was saying before, 80% of the functionality, strong candidates. High volumes of historical data are available where you can do really good trends analysis, taking really high volumes of transactional data and for the most part, structured. On the systems of engagement side, you have a growing landscape of various channels of interacting in social media but contrasting the systems of record, enterprises are just coming to grasp with the availability of the data. They may be engaged but then are they doing something with the data that is being put forth by the systems of engagement? It would be naive to assume that you can just work with systems of engagement and not have to rely on the systems of record. They are not going away. They are here to stay. The art really is in ensuring that you leverage the instant access you have to the engagement type of data you get from social media and then also integrate with the right business perspective against the backdrop of historical transaction data using the past and the current to predict the future. The high volumes of data that are generated in the systems of engagement also provide you some automatic insight into the mindset. It's one thing to play psychologists and then try to penetrate the human mind of the person sitting in front of you but suddenly if you have 50 people, 100,000 and you want to see what the general mindset is, manually just interviewing each one is not going to cut it but then that's the type of underlying sentiment that you can get with the high volumes of data that are generated through the systems of engagement with the right tools. Can I ask a question, Bob Boyzman? Sure, go ahead. Could you ask that I go back to the previous slide please? Systems of engagement. I try to basically pass by systems of information systems, scatter systems, system control and data acquisition as well as social media. So what would you call it? Is this term widely used system of engagement at this point in time? This is more, I'm coming at this from an HP angle. So HP classifies it systems of engagement and systems of record. I cannot speak directly to its prevalence in the industry at large. Certainly I'm not aware of whether I triply use this or not. This is more from an HP perspective. Because I triply in their big data one, they classified all the same as right now if not to confuse the clients who are going to be seeing the white paper. This is the first time I hear of a system of engagement. So anyways, I'm not saying it's right or wrong. Is anybody else, any comments on that? It's not a question. Yes, Stuart, I just wanted to ask whether anybody heard of the use of systems of engagement? I have actually. It's a slightly different definition, but I have actually heard of it, yes. Okay. I don't really use that same context, but it's just a wording of the agent that I definitely see where it is, yes. Yeah, and also it is just a way to classify the types of systems we are dealing with and to provide a foundation for the use cases that I will be walking through later. And if there are alternative terms, those are quite welcome as long as it is the same concept. Well, I think probably we say systems of record and information systems of engagement that might be the status of the system, data acquisition, the social media. You classify it that way. I would just like to use, right now the landscape is pretty all over the place, so language is going to become, just to make sure we'll understand on that one. Sure. So from an open group perspective, if you are suggesting that we should standardize on the representation of the various systems and you're proposing the IEEE landscape, I'm completely on board with you. What I'm sharing here is the HP perspective based on the Meg Whitman presentation on big data use cases. So that's where this is coming from. Okay, so sorry. Okay, I'll back up. Okay, thanks. Sorry. I think it's a good observation, you know, from a taxonomy perspective. You know, I think our big data teams and open groups will note this comment by you. Thanks. So moving on, but thanks for the observation anyway. Moving on, the idea here is that, you know, when it comes to leveraging data to realize information, you can realize what is going on now and also use the data to predict the customer minds that let us say, what is the customer going to think in the future? So that is really where, you know, predictive analytics is an area where both systems of engagement with the context that the systems of record provide can be applied to realize future predictions. This is kind of speaking to the volume at which, the data at which the data is growing, and it is important that the right culture exists in the enterprise with the right tools, the right technologies to deal with this type of data and the volume that you're seeing here because it's only going to grow. And unless enterprises put the right mechanisms in place in a timely fashion, it is going to be a lost opportunity because they're not going to have the value that they would have realized had they had the right tools in place. So some enterprises are already late is my point. None. Is there a particular definition you're using for real life speeds as opposed to real time? Are you looking at a particular slide when you ask? The bottom left corner there, that one. Oh. It is more to the point here, Larry, right? Yes. Yeah. So the point here, Larry, is that, you know, you need access to the information at decision time when you need to make the right business decisions, having that access to the right information at the right time. That's really what the real life speed is talking to. More time is still. Yeah. Got it. So these are just some example channels of why and how the online interactions continue to increase volumes of data. And so this is a, you know, pictorial representation of environments. You know, there is not much. It's an information poor environment. But the real world today where we are, and this is something that, you know, we can deal with. We have dealt with in the past. But look at where we are today. This is the types of applications that are out there and that we need to work with on a constant basis in order to have access to data. Now having access to the right data matters at the right time, which is really what that decision time point was about earlier. So I don't know how many of you were at the Philadelphia Conference, but Michael Cavaretta had delivered a keynote. I had blogged about that. And one of the statements in the abstract was, you know, big data is basically, you know, it's nothing unless it can support analytics. So I'm providing this background because the use cases that I will be walking through is basically leveraging the systems of engagement in some cases, the systems of record and then, you know, combinations that are off to predict and also understand the current mindset. So these are all reinforced by other open group presenters, keynotes and so on as you can see here. So predictive analytics, traditional approaches just to kind of predict, you know, what the customer mindset is and so on. The traditional approaches are to, you know, maybe form a focus group, identifying the experts, asking the consultants, and then, you know, some human interaction involved, and then, you know, sometimes take a vote. These are some of the techniques that have prevailed over the years. But today, there is a lot of scope for innovation where you can actually leverage the data to cultivate the most valuable asset in your enterprise and that's information. So you can go to the place where the data is being generated and then apply complex analytical techniques to that data right then and there. I'm not going to get into the techniques itself. There is a whole science behind that, but that is kind of the backdrop for, you know, where we were and where we can be and where we are in some respects today. So just to take some comparisons here, in the traditional environment, if a customer, you know, is waiting and the customer lodges a complaint with the service provider, the service provider typically, you know, takes remedial measures, you know, with a letter of apology if at all and so on. But let's say, you know, today, what the customer does is they would tweet that I'm not getting service for my car. I've been waiting here and no one has really come to help me out. And suddenly it goes viral and the CEO comes to know and he calls the CIO asking, why is, you know, why is my name or company's name in the social media? And if the CIO says that, you know, I don't maintain Twitter that is really out there and the response will be, well, you do now. So that is how, you know, in real life scenario systems of engagement, the social media channels are impacting, you know, in real life. So in other words, in this particular case, CIOs need to be ahead of the game and make sure that they are monitoring the social media channels, leveraging the data that is generated to understand the customer mindset before, you know, it explodes into a situation that they cannot handle effectively. Let's say a package is being delivered, you know, and the homeowner is not there, the courier sends the package back to the sender. That's more operating in the traditional environment. But with instant message communication, if the courier is able to, you know, locate the consumer and then deliver the package to a more convenient location, the courier has actually used the data that is available to them, used channels where they can have access to the right people at the right time, the decision time, and thus realize the return on the information that they can get. There are hyperlinks, by the way, so you can get to the actual content, but basically the ROI has taken a new definition, return on information. It just went up because they still delivered the package on time and also engaged the customer, leveraging social media channels. The Oscar is just an example. Earlier traditional environments would be more about, you know, moviegoers picking their winners based on their perceptions. And then maybe some experts out there and then they watch the ceremony and that's kind of how one would predict, you know, who is likely to, you know, which movies are going to win the Oscar. But if movie buffs were sharing their opinions, the enterprises can, you know, we can leverage such information to kind of predict where exactly, you know, which movie is going to get the Oscar or what are the top rankers and so on just based on the public sentiment, which the public is not shy to share. These are some of the, how, you know, big data and then the availability of big data in social media is impacting the various scenarios where traditional environments are more reliant on structured, you know, time-intense approaches using systems of record, where our systems of engagement actually allow you to do, you know, in a much more dynamic fashion. Some of the predictive techniques are, you know, applying sentiment analysis. So you would actually mine the text for various opinions, selection of opinions. You can see if there is a, you know, the polarity of the opinions, the positive or negative. And then you can also gauge tweets, for example, on the ratio of the positive to negative tweets and how many are neutral. And this is all very useful information, engaging the mindset, not just for today, but also what the mindset is likely to be. NASCAR, for example, they would actually, they used social media channels to determine how the public would react to a new model from Chevy. And, you know, how will that model be perceived before it's even in the race? So these are some of the analytical techniques that could be applied to gauge where the sentiment of the public is. So now I'm going to come to the real, you know, the use cases, which was the request, but I did want to provide that backdrop before getting into the use cases themselves. So there are three use cases, and then I will also outline some scenarios. But the first use case is predicting the box office sales for a movie. So even before it's released, getting a sense for how well the movie is going to do well and, you know, what are the likely box office sales in the very first weekend of its release. The next one is about placing bets on, you know, the business events that are happening. This was done within HP to predict outcomes. And the presidential campaign last year leveraged the, you know, tons and tons of data, I think, 180 million voters to analyze the sentiment of where the voting base was so that they can take remedial measures to address the voter sentiment. They used big data analytical tools to determine what, you know, based upon their overall campaign strategy. Well, so the sentiment of the voters defined their campaign strategy, and then they took proactive measures to address that sentiment and take remedial measures. So these are the three use cases I'll be walking through in more detail. So this is the case. This date does days back a couple of years. So multiple movies, the sentiment around multiple movies were analyzed about 24 of them. 2.89 million tweets were looked at from 1.2 million users. So you see the predicted and actual amounts for the movies in the first weekend. So it was not that far off. It was, you know, give or take, it was very much within 90 to 95% of the predicted amount, the actual amounts themselves. So this is a good example of how the right tools can be used to apply data that is freely available. We didn't have to make phone calls to 1.2 million users to ask them what they thought about the different movies. The data is out there. So the use case is really about, you know, what are some someone like, you know, 20th century Fox or, you know, movie producers and so on. They would be very interested in knowing, you know, what's the likely outcome and, you know, do they have to adopt other marketing techniques and so on? And this is just an example. But such techniques could be applied from a marketing perspective from, you know, in other domains as well. Because the public is not shy. You are going to get the data whether you like it or not. The question is, what are you doing to leverage the data with the right tools applying the analytics so that you can turn that into business value for yourself? So the second use case is more about how, you know, the prediction on the bottom line. So HP's senior fellow, Bernardo Huberman, has orchestrated this method of having multiple leaders from HP to place bets. And based upon price ranges, there is a way you can win. It's kind of a gamification, if you will. And this prediction actually beat the forecast. It was a different technique that was used to prove the point that you can actually use such techniques like online betting and so on to, you know, in the prediction market so that you can leverage the sentiment. You can leverage the individual opinions of a wide mass of perceptive public, people in responsible positions, subject matter experts and so on, to predict the outcome. This was the other use case. The voter sentiment, so here the Obama campaign had a 180 million voter file with tons and tons of data about the voter preferences, the volunteers, donors and so on. And their objective was to get the voters who voted in 40 years back to do it again. Now, the question was, you know, are they having the same mindset as they did before? And based upon various parameters, they analyzed this information across 180 million people. They used an analytics platform to do that and then leveraged the engagement that they had with the voters and used, this is a classic example of the voters' thoughts through systems of engagement being built into the baseline systems of record that they had on them and then extracted an integrated perspective to realize valuable information. This is not to say that, you know, every presidential campaign that leverages, applies such techniques is going to be a winning campaign. It is just an after-the-fact analysis of how the data that was available and it was not, you know, no one really said, served up the sentiment, so to speak. The data was available. The information that the campaign realized was where is the voter mindset today? And applying analytical techniques, how can that sentiment be changed to a favorable one for the campaign itself? And what are the measures that they have to take? That's the type of information that they got out of the high volume of data that was available. So it took the right technologies, the tools, the solutions and so on. So that's really the point here. Applying complex analytics on the high volume of data that is available in order to turn it into business value. So that is the third use case. So here I'm going to be talking more about various scenarios across different industries. I have just taken some examples of, you know, going across different industries. That is not to say this is not necessarily the only industries where big data has had an impact. So in transportation, the impact of big data there is, you know, connection is very important. So from a platform 3.0 perspective, you know, being mobile and not losing the connectivity even when you are traveling. You're sitting in the aircraft. If you have access to Wi-Fi and, you know, you can still access your social media channels and get the data you need about the place you left or the place you're going to or your business transactions, whatever it be, staying connected is a key for enterprises who are in the transportation industry. To airlines, it matters that the customers of airlines, it is very important to them that they continue to stay connected even though they may be up in the air and, you know, quote-unquote disconnected from the rest of the world. But that's why the manner in which big data is influencing how airlines work with their customers is, you know, by providing access, by making sure that they stay connected. So the call to action for enterprises in the transportation industry is to ensure that, you know, whether you are at the gate or in the aircraft or just landing, as long as the connection is there and there is no disruption, you have a happy consumer. That's kind of a minimum requirement nowadays from customers. So that's how big data is impacting the... That's just an example of how big data is impacting the transportation industry. Let's go to communications. And each of these, by the way, I'm not clicking on them, but you will get more details about, you know, the thought process behind that. Communications is... So as communication service providers, when you deal with subscribers to a mobile service, you know, CSPs, you have tons and tons of data about the consumer buying patterns, interests, and so on. The question is, enterprises, you know, how are they profiling the customers so that they can ask the, you know, the next logical question, you know, you have this but do you need that and have you considered this? So that type of a smarter approach to profiling customers, again, leveraging the high volume of information, the data that is available, and processing that into valuable information about your customer enables you to provide better service overall. So the question there is, are enterprises taking a smarter approach when it comes to customer profiling? So that's the manner in which big data has impacted the communications industry. When it comes to retail, the consumer, just by, you know, clicking on the shopping carts and, you know, making the selections, personalizing what they need and their buying patterns and so on, the consumer themselves is a living hub of data about themselves. So are retailers leveraging this data? The call to action for retailers is to apply the right analytic techniques to not only to the data, to not only find out, you know, what their buying pattern is today, but more importantly, what's the consumer going to buy next week? What are the shopping trends, let's say the next holiday season? That's the type of information that retailers can benefit from. Again, the call to action is, are they applying the right techniques? Because the question is not whether the data is available to them. The consumer, you, me, everybody else is providing and generating that data every minute. But are the retailers applying the right techniques to maximize the business benefits? Insurance. So insurance is one world where you would need to collect data about, you know, the personal information, whether it be business or, you know, individuals, the types of, you know, various dynamics, families, events, life-changing events and so on. And insurance companies need to come up with different programs and incentives that would actually are aligned to the needs of the customer. Policies that actually add value from the customer's perspective. So insurance companies that don't do this are likely to fall behind the times because the next company that actually brings up the policy and the product that actually provides that type of competitive edge are the ones that are going to be here for a longer time. So for insurance companies, the question really is, are they leveraging the data kind of to ensure themselves against, you know, a lost opportunity? Are they doing that so that they don't lose their competitive edge? Energy. So here there is tons of data to be captured. I think by now with smart meters and so on, you know, it is no longer manual, but still, you know, it is good to collect the data. But again, there are energy companies taking the data and then processing it to predict what the energy needs are going to be in the future. And do they have the infrastructure to support that need? What's the rate at which energy consumption is going to grow? So that's one way that companies can change the game, so to speak, from an energy perspective. So this here actually summarizes all of this and analyzes the impact of, you know, from a big data perspective on various scenarios in different industries. So what I have done so far is gone through some use cases and then also highlighted certain scenarios from an industry perspective as well as calling to action, you know, for the different enterprises in various industries to take a step back and ask themselves the question. You know, the question that pertains more to them. What's interesting about this is, you know, this is not to imply that, you know, these are the only questions to be asked or it is not necessarily specific to a given industry. Let us just say that it applies more in those industries so any of these questions could really be asked and should be asked by any of the enterprises in any industry. But when it comes to transportation, connectivity matters. Profiling customers matters more so from a communication perspective. Retail, you want to analyze the buying patterns. Insurance, you want to retain the competitive edge. New products. And energy, you do need to have the game-changing infrastructure to monitor the consumption pattern for energy resources. So those are some of the scenarios. I think I have, yeah, so here are some references about, you know, big data in general. These are multiple blog posts that will call out various perspectives of big data. So that will supplement the use cases and scenarios that I walked through. I think I am at the end actually. So time for Q&A. We have plenty of time, more than happy to engage in discussion on any of the, you know, topics that I just covered. That was a great conversation. So Kapil, let me ask you, is this the kind of content that you were, you know, you had in mind, we did not have any detailed exchanges on the topic itself other than one or two lines. So this is what, you know, I had in mind. This certainly looks to me to be very relevant model. And I would think that it is certainly from an open platform 3.0 point of view, it's something that I'm sure is very beneficial to have available to the forum. One thing I note, although this is called big data, the case studies also very largely seem to feature social media. Do you see a sort of, this as being the main source of big data or would you see applications using big data coming from other directions? Yeah, great question, Chris. Right or wrong, there is an automatic association of social media by and large with big data, but if you make a good point, it is not necessarily only social media, in fact, going back to my earlier point, systems of records do have tons and tons of data that have accumulated and grown over the years. So the voter sentiment use case was really based upon, you know, legacy data that was already available, complemented by social interactions. So it is a combination. That said, I will say that there is, the idea is definitely acting as a catalyst. It is one of the prime sources for the explosion of data growth because the generation of data, the data at which data is getting generated with each tweet and Facebook posts and so on, there is just no control. And also the volume is also augmented by the unstructured data, you know, videos and audios and bitmaps, images and whatnot. And space no longer being as much at a premium as it used to be with multiple storage service providers providing this in an economical fashion. There is no limit to the amount of space available. So those are all catalysts as to why the big data is growing at the rate at which it's growing. But your point is more than valid, you know, it is not just social media. How about that? Did you mention that there was a, I found one of the documents for the working group that was Amsterdam is a smart city? That's for me. Yeah, that was a really good one. I went through that site and, you know, I think we should, like I said, that's more than just social media. That's situational awareness is the, there's another term that we really should take a look at. You basically have, you know, collaboration, cooperation, smart decision for that. You're inferring it. Oh, that's right. Anyways, I liked that one on the, Amsterdam was good. Also Harvard Business School, I think it was really helpful management as a shop court hypothesis. 7-11 is a good case study and to a large extent, it's an integration of all the data from the supply chain and the like to basically empower the shop court in every one of the tens of thousands of franchises globally. That might be another area where we're dealing more with control systems data as distinct from social media. What I think from the point of view of the use cases that we've been doing for platform, for the platform as a whole, I think it'd be very useful to add the ones that Nadal has presented today to the list that we've got because they bring in other things that we haven't, I suppose because of the specific focus on big data, but they bring in aspects that we certainly haven't covered up for now and connect quite well with some of the other things. I think it's a really useful contribution. Thank you. Thanks much. We just had a serious bus train collision in Ottawa. It was an interesting case study where you basically had a black box on the train, a black box on the bus, and a black box at the crossing. Had they been integrated, the brakes would have been automatically applied to the bus because the bus ran into the train, not by bus. Is this an area we should start to look at, not the big data, but integration of big data for safer outcome? Bob, this is Ron. I like that idea, and I would categorize what you just described as a sensor-based big data. Sensors are out there everywhere, and integrating sensors into decision support systems is what I think you're really trying to describe. Or even mechanical systems. Right, right. But it's sensors feeding a decision support system. Yeah, precisely. And some of these decision support systems, like automated avionics, can act without a human loop under certain circumstances. Right, that's a huge category of big data. Yeah, but here they're trying to... Yeah, the analytics there is totally different than looking at tweets and analyzing positives and negatives of tweets. It's a completely different entity. It's a different paradigm. Well, the underlying technology is not all that different. It's a function of integration, and that's where the own saying that could be a service that the open group could basically provide in terms of standards. Right. Yeah, sensors have been around for much longer than social media. So I would say that social media recently has acted as a catalyst, but it has given visibility to other sources of big data that have been around for a much longer time. It's kind of bringing it together under the big data umbrella. Right. In that sensor I did, like air traffic control context, airspace density and airspace management, the use case there. Well, Harvard defines... Sorry, go ahead. No, that's all right. If you carry on, I was going to introduce a slightly different thread, so carry on. Well, just the Sloan School of Management has freed you on their analytics versions. The decision judgment calls, they basically define the decisions of automated, semi-automated, and basically advisory. And this is a good way of determining. And by dividing the systems into traditional information systems, your system control and data acquisition systems, which are sensors of one class of them, and the social media, by using that division, we're encompassing... I think we'll make it much more explicit of what we're trying to encompass. Okay. So the different thread, I was slightly different thread, I was going to introduce. One of the things I noticed, Naden, about some of your examples was that the basic information was collected by text analysis, and that was some quite clever analysis programs that were determining sentiment from the text. Now, there are an awful lot of video cameras around, and I'm just wondering how close we are to being able to use analyzed video as a source of big data. Yeah, so... Have you heard of anything happening on this? Yes. I did not cover use cases where you can actually, you know, have image recognition from videos, or even you look at a static photograph, and then using tools you can actually discern who that person is and then bring up the video that applies to that particular person, or maybe a basketball star, or a sportsman, or movie actor, and so on. You can... It's as good as recognizing this person to be whoever they are and then doing a Google search, so to speak. That type of technology and image recognition software does exist, and yes, there are use cases that apply to that as well. There are specific tools that are used for that purpose. It certainly falls within the realm of big data. I just... That just was not one of the use cases I picked. I should have, in response to that. David, actually, you raised an interesting issue. Harvard Business School is calling that something called datafications or London School of Economics. It's called that datafication where they're transforming unstructured data into semi-structured and structured data. Could that be a use case? Just call it datafication. Does everybody understand what I mean? You're making it from machine unintelligible to machine readable and machine readable and processable. Datafication. It could be... I have seen a different term in the Harvard Business Review, in the blog at least, informationalization. That's what Thomas C. Redman introduced in the Harvard Business Review. That could be another thing to consider as well, but that is more than just looking at unstructured data and then realizing structured data from that. It's really adding context and meaning to the underlying data so that you can have information that generates value. That's really what that is. Okay. Well, I think it's early stages right now. There's a lot of terms flying around, and what we should do is probably in our dictionary is show synonyms as well. That'd be really useful. My humble request would be let us not introduce yet another term. There are enough of those out there. Please, yeah. I agree. In terms of terms, there's also machine-to-machine. So many other terms are also used, because we have been gathering data for a long time in the factories and missionaries. And everybody is now waking up to the realities. Oh, there's not a big data. So many use cases are coming out of the closet. I'll send a copy of the reference I have. It's Oxford Internet Institute. And the economist that published the book called and one of their major focuses is data fiction. It might be an interesting, and it's already been published also in Foreign Affairs and some other magazines on the business side. So anyway, I think that would be an interesting, that would be a really interesting scenario. Yeah, we should all say thank you very much, Madan, for a very, very thought-provoking presentation. Thank you very much. You're very welcome, Chris. Thank you. Thank you.